What district heating share is optimal for a municipality? A holistic approach including supply temperatures

Municipal heat planning requires granular differentiation between network-based and building-specific energy supply in urban energy system optimization models (UESOMs). However, most energy hub approaches rely on predefined separation and neglect district heating network (DHN) temperature levels. Additionally, simplified temporal resolutions of heat pump coefficients of performance (COP), limit accurate modeling of the overall system performance. A holistic methodology for optimal urban energy system planning is proposed, integrating DHN design into the energy hub framework. The scalable Mixed-Integer Linear Programming (MILP) approach enables the joint optimization of DHN expansion and building-specific heat supply, as well as the evaluation of DHN temperature levels and the temporal resolution of COPs. The framework is applied to a case study in southern Germany, and results show that DHN temperature levels can have a significant impact on both system configuration and costs. Increasing the DHN supply temperature from 60 °C to 90 °C leads to a 20€/MWh increase in the levelised cost of heat and a decrease in the optimal centralized heat supply share from 63.8% to 19.8%, shifting from centralized to building-specific solutions due to the less efficient integration of renewable energy sources. The availability of baseload renewables, such as deep geothermal energy, consistently increases DHN deployment by 5% independently of supply temperatures. Moreover, modeling time-varying COPs avoids the systematic underestimation of heating costs that occurs when constant efficiencies are assumed. The framework can provide decision-making support to municipalities, engineers, and energy planners when evaluating heating supply goals. • Integration of DHN expansion vs. building-specific supply into the energy-hub approach • Assessment of high temporal COP resolution and their influence on DHN expansion • DHN temperature levels and their effects on the integration of renewable heat sources • Scalable and applicable to large urban areas.